arXiv Machine Learning July 9, 2026 By Faranak Hatami, Mousa Moradi

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions

Read the original on arXiv Machine Learning →

arXiv:2607. 07611v1 Announce Type: new Abstract: Background: Graph neural networks improve computational prediction of polypharmacy side effects, but standard binary cross-entropy training allocates equal capacity to well-classified and difficult examples, potentially missing clinically significant interactions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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